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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an
area of interest for quantification of regional cardiac function from balanced,
steady state free precession (SSFP) cine sequences. However, currently
available techniques lack full automation, limiting reproducibility. We propose
a fully automated technique whereby a CMR image sequence is first segmented
with a deep, fully convolutional neural network (CNN) architecture, and
quadratic basis splines are fitted simultaneously across all cardiac frames
using least squares optimization. Experiments are performed using data from 42
patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control
subjects. In terms of segmentation, we compared state-of-the-art CNN
frameworks, U-Net and dilated convolution architectures, with and without
temporal context, using cross validation with three folds. Performance relative
to expert manual segmentation was similar across all networks: pixel accuracy
was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU
across foreground classes only was ~85%. Endocardial left ventricular
circumferential strain calculated from the proposed pipeline was significantly
different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in
agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method
based on a novel deep convolutional neural network (CNN) designed for the 2017
ACDC MICCAI challenge. The novelty of our network comes with its embedded shape
prior and its loss function tailored to the cardiac anatomy. Our model includes
a cardiac centerof-mass regression module which allows for an automatic shape
prior registration. Also, since our method processes raw MR images without any
manual preprocessing and/or image cropping, our CNN learns both high-level
features (useful to distinguish the heart from other organs with a similar
shape) and low-level features (useful to get accurate segmentation results).
Those features are learned with a multi-resolution conv-deconv "grid"
architecture which can be seen as an extension of the U-Net. Experimental
results reveal that our method can segment the left and right ventricles as
well as the myocardium from a 3D MRI cardiac volume in 0.4 second with an
average Dice coefficient of 0.90 and an average Hausdorff distance of 10.4 mm.Comment: 8 pages, 1 tables, 2 figure
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